Avoiding Virtual Obstacles During Treadmill Gait in Parkinson’s Disease

Falls often occur due to spontaneous loss of balance, but tripping over an obstacle during gait is also a frequent cause of falls (Sheldon, 1960; Stolze et al., 2004). Obstacle avoidance requires that appropriate modifications of the ongoing cyclical movement be initiated and completed in time. We evaluated the available response time to avoid a virtual obstacle in 26 Parkinson’s disease (PD) patients (in the off-medication state) and 26 controls (18 elderly and 8 young), using a virtual obstacle avoidance task during visually cued treadmill walking. To maintain a stable baseline of stride length and visual attention, participants stepped on virtual “stepping stones” projected onto a treadmill belt. Treadmill speed and stepping stone spacing were matched to overground walking (speed and stride length) for each individual. Unpredictably, a stepping stone changed color, indicating that it was an obstacle. Participants were instructed to try to step short to avoid the obstacle. By using an obstacle that appeared at a precise instant, this task probed the time interval required for processing new information and implementing gait cycle modifications. Probability of successful avoidance of an obstacle was strongly associated with the time of obstacle appearance, with earlier-appearing obstacles being more easily avoided. Age was positively correlated (p < 0.001) with the time required to successfully avoid obstacles. Nonetheless, the PD group required significantly more time than controls (p = 0.001) to achieve equivalent obstacle-avoidance success rates after accounting for the effect of age. Slowing of gait adaptability could contribute to high fall risk in elderly and PD. Possible mechanisms may include disturbances in motor planning, movement execution, or disordered response inhibition

Overground versus treadmill walking in Parkinsons disease: Relationship between speed and spatiotemporal gait metrics.

BACKGROUND: Treadmills provide a safe and convenient way to study the gait of people with Parkinsons disease (PD), but outcome measures derived from treadmill gait may differ from overground walking. OBJECTIVE: To investigate how the relationships between gait metrics and walking speed vary between overground and treadmill walking in people with PD and healthy controls. METHODS: We compared 29 healthy controls to 27 people with PD in the OFF-medication state. Subjects first walked overground on an instrumented gait walkway, then on an instrumented treadmill at 85%, 100% and 115% of their overground walking speed. Average stride length and cadence were computed for each subject in both overground and treadmill walking. RESULTS: Stride length and cadence both differed between overground and treadmill walking. Regressions of stride length and cadence on gait speed showed a log-log relationship for both overground and treadmill gait in both PD and control groups. The difference between the PD and control groups during overground gait was maintained for treadmill gait, not only when treadmill speed matched overground speed, but also with ± 15% variation in treadmill speed from that value. SIGNIFICANCE: These results show that the impact of PD on stride length and cadence and their relationship to gait speed is preserved in treadmill as compared to overground walking. We conclude that a treadmill protocol is suitable for laboratory use in studies of PD gait therapeutics

Postural instability in Parkinson’s disease assessed with clinical “pull test” and standardized postural perturbations: effect of medication and body weight support

ObjectiveThis experiment tested if balance performance differed between a standardized treadmill surface perturbation task and a clinical pull test and was affected by medication or the presence of body weight support in people with Parkinson's disease (PD).MethodsTwenty-seven individuals were tested (14 PD in both ON- and OFF-medication states). Clinical pull test and rapid forward (backward fall) translations of the support surface were applied to induce postural reactions requiring at least 1 step to restore balance. The effects of pull type (clinical vs. treadmill), partial bodyweight support (0 vs 20% body weight) and group (control, PD ON-meds and PD OFF-meds) on reactive stepping as well as practice/learning effect were examined. The number of steps taken and the first step duration were entered in linear repeated-measures mixed-effect models separately.ResultsThe effects of pull type, group, and bodyweight support were all significant in both metrics, as was ON- vs. OFF-medication. A significant interaction term (group x pull type) was found in the first step duration, showing that the group difference was greater in treadmill compared to the clinical pull test. A significant practice effect was also observed within and across testing sessions.ConclusionsA standardized treadmill perturbation performed slightly better than the classical pull test in distinguishing between groups, and partial weight support did not substantially degrade the test's performance to detect the balance deficits in people with PD

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable